Feature-Based Resource Allocation for Real-Time Stereo Disparity Estimation
Bibliographic record
Abstract
The most accurate stereo disparity algorithms take dozens or hundreds of seconds to process a single frame. This timescale is impractical for many applications. However, high accuracy is often not needed throughout the scene. Here, we investigate a “foveation”approach (in which some parts of an image are processed more intensively than others) in the context of modern stereo algorithms. We consider two scenarios: disparity estimation with a convolutional network in a robotic grasping context, and disparity estimation with a Markov random field in a navigation context. In each case, combining fast and slow methods in different parts of the scene improves frame rates while maintaining accuracy in the most task-relevant areas. We also demonstrate a simple and broadly applicable utility function for choosing foveal regions, which combines image and task information. Finally, we characterize the benefits of defining multiple individually placed small foveae per image, rather than a single large fovea. We find little benefit, supporting the use of hardware foveae of fixed size and shape. More generally, our results reaffirm that foveation is a practical way to combine speed with task-relevant accuracy. Foveae are present in the most complex biological vision systems, suggesting that they may become more important in artificial vision systems, as these systems become more complex.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".